The present invention relates to a method for conducting a process in an automatically controlled system. At least one selected process parameter is precomputed at the beginning of a process run. A mathematical model of the process, supplied with input values and implemented in a computing means, is used to precompute the at least one process parameter. The system is preset with the precomputed process parameter. The input values and the process parameter are measured during the process. After the process, the precomputed process parameter is adaptively improved based on the measured process parameter and based on the measured input values supplied to the model. The present invention also relates to a device for implementing a control method.
A method and device for conducting a quasicontinuous process in an automatically controlled system are discussed in the German Patent Application No. 40 40360. Such processes typically include rolling trains wherein each pass of the rolled strip forms a process cycle (hereinafter "a process run"). Like all actual industrial processes, these process runs are time-variable. In conducting such processes, the system controlling the process must be preset before each run. Therefore, unlike traditional closed-loop control, control must precede the actual process. The control system is preset because in industrial processes, controlled values can often be measured only indirectly and not directly at the point where the process is affected. Therefore, direct closed-loop control is not possible in these instances.
The system controlling the process is preset, in a known manner, by precomputing selected process parameters according to pre-established input values, or initially estimated input values, or both, based on a pool of relevant mathematical models of the process. The system is preset using these parameters. Since mathematical models of the process to be conducted can only approximately define the actual process, the model must be adapted to the actual process events. To adapt the model, the process parameters and the input values are measured directly, or indirectly by precomputing other measured values, during each process run.
When the process run is complete, the precomputation performed with the mathematical models is repeated within the framework of a postcomputation done based on the input values measured at that time. The variable model parameters of the mathematical model are adaptively modified, based on the deviation between the computed process parameters and the measured process parameters, to reduce the deviations. The model parameters thus adapted are available at the beginning of the following process run for precomputing the process parameters.
Despite adapting the process model, the quality of the precomputed values of the process parameters, and thus of the presetting of the system, depends mainly on the quality of the model assumptions. As a rule, the model assumptions are difficult to make and may be subject to errors. Furthermore, adapting the model parameters to the model run continuously and in real time, i.e., on-line,requires a great amount of computing resources.
The goal of the present invention is to improve the precomputed values of the process parameters.